A Complete Guide to Generative Engine Optimization (GEO) - The Future of New Gen SEO

Table of Contents 

1. Introduction to Generative Engine Optimization (GEO) 
2. How Generative AI Processes and Responds to Queries 
3. Traditional SEO vs. New-Gen SEO 
4. GEO Ranking Factors  
5. What Types of Searches Trigger AI Overviews? 
6. Industry-Specific Touchpoints for GEO 
7. Data Sources of AI Tools like Chat GPT 
8. Optimizing for AI Models (PaLM 2, MUM, Gemini) 

With the rise of AI-powered search engines, traditional SEO practices are evolving. Generative Engine Optimization (GEO) is a modern SEO strategy focused on optimizing content for AI-generated search results, ensuring websites remain visible in a new era of search.   

How Generative AI Processes and Responds to Queries 

Artificial Intelligence (AI) systems process and respond to user queries through a series of structured steps designed to understand, retrieve, and present information effectively. Here's an overview of these steps: 

1. Query Submission & Understanding: Upon receiving a user's query, the AI analyzes its intent and context using Natural Language Processing (NLP) techniques. This involves parsing the language to determine the user's specific needs and any underlying broader intents. 
2. Content Gathering: The system retrieves relevant information from various sources, such as web pages, articles, images, and videos, to address the query comprehensively. 
3. Additional Processing: AI considers related searches and anticipates follow-up questions users might have, ensuring a more thorough and proactive response. 
4. Summary Generation: Advanced models like Gemini and PaLM 2 synthesize the gathered content into concise and helpful summaries, making the information easily digestible for users. 
5. Multi-step Reasoning: For complex queries, the AI breaks down the question into smaller components and addresses each step-by-step, ensuring a comprehensive response. 
6. Source Integration: AI overviews pull information from traditional search results, linking back to the original websites to provide credibility and allow users to explore sources further. 
7. Response Customization: The AI dynamically tailors its responses based on the specific context, adjusting tone and information to align with the user's needs and preferences. 
8. Presentation: The final response is displayed in a user-friendly format, incorporating text, images, lists, and other elements to enhance readability and engagement. 

By following these steps, AI systems aim to deliver accurate, relevant, and user-centric responses to a wide array of queries. 

Traditional SEO vs. AI Overview SEO 

GEO Ranking Factors  

Google's Search Generative Experience (SGE) ranks content based on several key factors: 

• AI Models: SGE utilizes advanced language models like PaLM2 and MUM to generate responses.  
• Core Ranking Systems: Traditional ranking elements such as PageRank, spam detection, and content freshness remain crucial.  
• Databases: SGE references Google's Knowledge Graph and Shopping Graph to provide accurate information.  
• Topic Sensitivity: For sensitive subjects, especially those affecting health or finances, SGE emphasizes reliable sources and may omit topics with insufficient trustworthy information.  
• Search Intent: Understanding whether a user seeks information, navigation, or a transaction helps SGE tailor its responses.  
• Multimedia Content: Incorporating visuals like images and videos can enhance content visibility in SGE results.  
• Structured Data: Implementing structured data aids SGE in accurately interpreting and displaying content.  

By focusing on these areas, you can improve your content's ranking within SGE. 

What Type of Searches Trigger AI Overviews   

AI Overviews in search results have been observed to significantly impact click-through rates (CTR). Notably, paid ads experience a decrease in CTR by up to 12 percentage points when an AI Overview is present. Conversely, AI Overviews can drive more traffic compared to traditional featured snippets. 

Data Sources of AI Tools like Chat GPT   

A Diverse set of data sources enable AI models like ChatGPT to generate coherent and contextually relevant responses across various topics. 

1. Publicly Available Internet Data: This includes a vast array of text from websites, articles, books, and other freely accessible online content. 
2. Third-Party Partnerships: OpenAI collaborates with external organizations to access specialized datasets that enhance the model's knowledge in specific domains. 
3. User-Generated Content: Data provided by users, human trainers, and researchers during the model's development and fine-tuning phases contribute to its training. 

AI-generated search results rely on various data sources, including: 

• C4 Dataset – This dataset is based on Common Crawl data, which is an open-source dataset.  

Below are few examples of top websites in C4 -

www.nytimes.com 

www.latimes.com 

www.theguardian.com 

www.forbes.com   

• Public Forums – 50% of AI responses come from platforms like Reddit, Quora, and StackOverflow.   
• Wikipedia & Knowledge Graph – Trusted sources for factual data. 

Optimizing for AI Models (PaLM 2, MUM, Gemini) 

Optimizing your content for AI models like PaLM 2, MUM, and Gemini enhances its relevance and reach. Here's a concise guide: 

1. PaLM 2 

Description: Excels in understanding and generating coherent long-form text with strong reasoning and factual accuracy. 

Optimization Tips: 

• Craft clear, well-structured content. 

• Use authoritative sources to enhance credibility. 

• Ensure grammatical precision. 

2. MUM 

Description: Handles complex, multimodal queries, integrating information from text and images to provide comprehensive answers. 

Optimization Tips: 

• Combine textual content with relevant visuals. 

• Address multi-part questions thoroughly. 

• Utilize structured data to clarify content relationships. 

3. Gemini 

Description: A multimodal model processing text, images, audio, and video, adept at interpreting ambiguous language and supporting creative applications. 

Optimization Tips: 

• Incorporate diverse media formats like videos and podcasts. 

• Provide descriptive alt text and filenames for all media. 

• Apply schema markup to enhance media discoverability. 

By aligning your content with these models' strengths, you can improve engagement and visibility in AI-driven search results. 

Frequently Asked Questions (FAQs) 

1. What is Generative Engine Optimization (GEO)? 

GEO is an advanced SEO approach tailored for AI-driven search engines, ensuring content visibility in AI-generated search results. 

2. How is GEO different from traditional SEO? 

Traditional SEO focuses on keywords and backlinks, while GEO optimizes for AI-generated search results, structured data, and natural language processing. 

3. Why is structured data important for GEO? 

Structured data helps AI models understand content better, improving search rankings and increasing visibility in AI-driven overviews. 

4. What industries benefit most from GEO? 

Industries like healthcare, finance, e-commerce, and B2B businesses benefit significantly from AI-powered search optimization. 

5. How can I optimize my content for AI-generated search results? 

Use structured data, answer questions concisely, focus on authority and trustworthiness, and optimize multimedia content. 

Note – The above data sets are basis on my research, findings, experiments and experiences on Multiple brands.